The Open Microscopy Environment is a project started by Dr. Ilya Goldberg at MIT four years ago in the group of Dr. Peter Sorger. The purpose is to develop an information framework for computational cell biology - a sub-specialty of bioinformatics called 'Image Informatics'. This open-source framework consists of a database, several analytic modules, and an application program interface (API) that ties the modules to the database. The database provides a semantic framework and a data model for biological information obtained by analyzing images. It also keeps track of the images themselves, and of all the analyses performed on them. The database is also the communication link between analysis modules permitting the multiplexing of analysis algorithms. Finally, the entire system provides a web-based user interface allowing for remote interaction. Over the past year we have made our first public release of this software targeted at end-users. This software functions as an image repository for very large collections of scientific image data. This software is currently deployed in several research labs with considerable imaging needs, and includes repositories well over a terabyte in size. Secondarily, a considerable amount of commercial interest in this software has developed over the past year, specifically in our universal XML-based image format. Manufacturers of microscopes and other high-throughput cell biology acquisition hardware that we've had contacts with include Zeiss, Perkin-Elmer, Amersham, Bitplane and Applied Precision. Currently Bitplane and Applied precision support our format in their commercial offerings. The second major effort in our group involves building information visualization tools for the OME platform. Dr. Harry Hochheiser has developed a visual image analysis chain builder that allows the user to place analysis modules on a canvas and connect their inputs and outputs into work-flows (analysis chains) of arbitrary complexity for execution by OME. Dr. Hochheiser is currently working on ways to visualize the results at each point along this work-flow. The third major effort in our group has been the application of machine learning techniques from the artificial intelligence field to the problem of image classification. These efforts have largely come to fruition over the past year and we are now able to classify a great variety of images, often even in cases where the differences are too subtle to be perceived by human observers. This effort involves Dr. Nikita Orlov and the newest addition to our group, Tomasz Macura, a graduate student in the NIH-Cambridge GPP program. We believe that this classification technique will prove immensely useful for high-throughput cell-based morphological screens.